Publication Date
Fall 2023
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering
Advisor
Stas Tiomkin; Gautam Kumar; Kah Chun Lau
Abstract
The design of appropriate control rules for the stabilization of dynamical systems can require quite substantial domain knowledge. Modern AI methodologies, such as Reinforcement Learning, are often used to mitigate the need for such knowledge. However, these can be slow and often rely on at least some hand-designed reward structure, and thus human input, to be more effective. Here, we propose an alternative route to construct rewards requiring only minimal domain knowledge, essentially relying on the structure of the dynamical system itself. For this, we use truncated Lyapunov exponents as rewards to calculate the stabilizing controller from samples. Concretely, the controller directs the system towards maximally sensitive states. This requires no domain knowledge and only the system dynamics and one parameter (the truncation horizon) to provide an effective stabilization behaviour.
Recommended Citation
Nguyen, Phu C., "Intrinsic Motivation by the Principles of Non-Linear Dynamical Systems" (2023). Master's Theses. 5466.
DOI: https://doi.org/10.31979/etd.v3uh-chkz
https://scholarworks.sjsu.edu/etd_theses/5466
